Purpose
This paper aims to review the critical technology development of avian radar system at airports.
Design/methodology/approach
After the origin of avian radar technology is discussed, the target characteristics of flying birds are analyzed, including the target echo amplitude, flight speed, flight height, trajectory and micro-Doppler. Four typical airport avian radar systems of Merlin, Accipiter, Robin and CAST are introduced. The performance of different modules such as antenna, target detection and tracking, target recognition and classification, analysis of bird information together determines the detection ability of avian radar. The performances and key technologies of the ubiquitous avian radar are summarized and compared with other systems, and their applications, deployment modes, as well as their advantages and disadvantages are introduced and analyzed.
Findings
The ubiquitous avian radar achieves the long-time integration of target echoes, which greatly improves detection and classification ability of the targets of birds or drones, even under strong background clutter at airport. In addition, based on the big data of bird situation accumulated by avian radar, the rules of bird activity around the airport can be mined to guide the bird avoidance work.
Originality/value
This paper presented a novel avian radar system based on ubiquitous digital radar technology. The authors’ experience has confirmed that this system can be effective for airport bird strike prevention and management. In the future, the avian radar system will see continued improvement in both software and hardware, as the system is designed to be easily extensible.
For the airports worldwide, it is important to establish a "passenger integrity system" based on the basic information of passengers and their related credit system. Correspondingly, this paper develops a new risk assessment model for the passenger graded security check by introducing several new technologies to obtain the passengers’ real-time status information as well as historical data. We first propose to deploy a variety of 5G-IoT devices to monitor the passengers in real time, including high-definition cameras, millimeter-wave security detectors, etc. We then rely on machine learning to analyze the passenger risk level and integrate improved analytic hierarchy process (AHP) with group decision theory, namely GD-AHP. According to the risk level, the passengers can be classified into known, ordinary and dangerous targets. The differentiated handling of different targets could significantly save the time of security check and improve the passenger experience.
Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.